Prior art physician certifying systems have traditionally relied upon written cognitive examinations as a method for the assessment of the candidate's (diplomate's) medical knowledge, or alternatively, oral examinations. Test formats such as multiple choice questions have well-defined operating characteristics and are reliable for determining a candidate's cognitive functioning in the test subject area.
However, these tools generally measure only cognitive knowledge, and provide at best a primitive ability to assess a candidate's problem-solving abilities.
Prior art physician assessment processes suffer from several disadvantages: 1) test development requires re-generation of an examination with new material on a recurring (usually annual) basis; 2) the use of multiple choice testing for assessing clinical problem solving is sub-optimal; and 3) the tests are generally incapable of presenting patients with symptoms responsive to physician interventions.
A plurality of computer programs and associated models have been constructed to simulate patient-physician interaction. However, many of these programs suffer from the problem that the clinical simulations are “hard-wired” in computer source code and thus must be reprogrammed for each new examination. Once a simulation is used widely, the examination is no longer secure thereby necessitating further simulation development.
Furthermore, prior art simulators are largely incapable of providing for interaction between a physician and a simulated patient. While some systems provide the ability for a simulated patient to present a symptom, and provide a rudimentary response to a user supplied intervention these systems do not have the ability to intelligently respond to a simple physician question such as, “Do you have pain anywhere?” and a follow-up detail question such as “What alleviates your pain?”
Responsive to the aforementioned disadvantages in the prior art, a computer implemented system and method for patient simulation that is capable of interacting with a user-physician is provided. The system is capable of instantiating medical knowledge as object-oriented data structures known as a knowledge base of family medicine, utilizing the medical knowledge structures to generate realistic clinical scenarios (simulated patients), accepting queries and interventions from a user based on symptoms presented by the simulated patient, provide detailed symptom descriptions to the user that are temporally and physiologically reasonable, and assess the candidate's clinical problem solving ability by determining the response to the simulated patient of a prescribed intervention.
One difficulty encountered when providing for simulation of patients for diagnostic purposes is the development of temporal events in a simulated patient such that a diagnosing physician may inspect the duration of events that impinge on the simulated patient's health and be presented with temporally reasonable reports that are descriptive of how a patient would feel given those events. In other words, a sophisticated patient simulator should be able to construct and present patient symptom histories that are both physiologically and temporally reasonable given the patient's health state and any interventions or courses of action that may have been prescribed by a diagnostician. Additionally, the modeling of concurrent diseases in the same patient requires the simulator to distinguish findings or patient symptoms that may be identical or substantially similar for the concurrent diseases, but require dissimilar treatment from the treating physician.
Furthermore, to enhance the ability of a system to examine diagnostic and health management skill sets, the system should simulate a patient that has the ability to communicate the presence of pain (a site), and the palliative, provocative, quality, radiation, severity and temporal details thereof. These details are sometimes referred to as the “PQRST” symptom details.
Thus a robust system should enable a simulated patient to include the presence or absence of a health problem, any findings related thereto, and the existence and effects of any interventions, prescribed or otherwise, over time. The patient simulator could then inspect these details and produce reports to the user that are temporally reasonable given the patient's history.
Prior art systems of patient simulation and interaction are generally incapable of producing a response to user queries that adequately describes the effects of provocative and palliative events over time, or even reasonably describes the net results of multiple events that may interact with each other. For example, the question “During an angina attack, what relieves your pain?” restricts any reasonable response from the simulator to the chest area, or wherever the simulated patient is feeling the angina, and the time to “during an angina attack”. While the query itself is detailed in its nature, it lacks a specific reference to time (and is of course nonsensical absent some angina in the simulated patient).
Furthermore, the user's query may require that the simulated patient summarize the overall results of multiple events such as angina attacks. In such a case, a complete response may include multiple parts, which may not be logically possible when taken separately. For example, in response to a query regarding angina over time, a patient may respond thusly: “Rest completely relieves my pain in about 20 minutes. Nitroglycerin completely relieves my pain in a few minutes, but not if I continue to exercise.” This response includes details about the patient's level of relief and how quickly it occurs under certain circumstances. Additionally, in order for the response to present a complete picture of the patient's pain, several details must be appended together.
Previously described mechanisms for answering queries (e.g. Sumner W., Hagen M D, Rovinelli R. The item generation methodology of an empiric simulation project, Advances in Health Sciences Education 1999; 4(1):25-35) severely limit a patient simulator's ability to adapt to important variables. A simple and obvious mechanism for describing temporal details is to record data such as the time that a health state begins during the generation of a virtual patient, then report the time elapsed since the health state began as a temporal feature of the patient. For instance, if the health state is hypothyroidism, and the user asks about fatigue, the simulator could report the duration of the virtual patient's hypothyroidism as a surrogate for the duration of fatigue. Similarly, the simulator could instantiate the hypothyroid state by asserting a fatigue score. This method, however, is inadequate to describe the end result of several diseases or several interventions affecting one symptom. For instance, a virtual patient portraying anemia, hypothyroidism, and depression may report fatigue, but the fatigue should not respond to any single treatment. Only simultaneous treatment of hypothyroidism, resolution or treatment of depression, and completed treatment of anemia should eliminate fatigue.
These variables may include the user's selection of interventions, the timing and strength or dose of those interventions, and the interaction of multiple diseases and interventions over time. Other important variables include the interval between queries, recall of previous query results, and descriptions of responses to individual interventions apart from the overall mixture of interventions. The invention described herein provides methods for each of these purposes.